Sentiment Analysis Study Tour Bus Ban on Twitter Using Support Vector Machine Method
-
Ony Hizri Kaifa Purba
*
State Islamic University of North Sumatera, Medan,
Indonesia
- Ilka Zufria State Islamic University of North Sumatera, Medan, Indonesia (*) Corresponding Author
Abstract
Study tour is an activity outside the classroom that has the purpose of learning about the process of something directly. This activity is usually carried out by the school once a year. This activity is not only a learning tool for students, but also a recreational activity.In this activity, there are many things that need to be prepared, such as transportation, lodging, meals, and so on. This is sometimes troublesome, because not all tourists or business people have the time and willingness to prepare it. Therefore, they need services during their trip. Especially now that it is even semester, where every school usually holds a study tour, as well as a final class farewell. As a response to concerns, some parents may choose to find alternative activities that are considered safer for their children, such as joining activities in the city or at school. Based on this need, it makes opportunities for business people engaged in the tour agency industry. SVM (Support Vector Machine) is a machine learning method that works on the principle of Structural Risk Minimization (SRM) with the aim of finding the best hyperlane separating two classes in the input space. Simply put, SVM (Support Vector Machine) has the concept of finding the best hyperlane, which serves as the boundary of two classes The results of sentiment classification on Study Tour Buses using the Support Vector Machine algorithm that matches the actual data amount to 176 data out of a total of 240 test data. It is known that of the 1200 data obtained regarding sentiment towards there are 519 reviews that are positive and 681 reviews that are negative.The accuracy value of the Study Tour Bus sentiment classification using the Support Vector Machine (SVM) algorithm obtained is 73%.
Downloads
References
B. T. Saputra and D. W. Ariani, “Optimalisasi Media Sosial sebagai Sarana Pemasaran Agen Perjalanan Atmaja Tour Bantul” 2024.
P. E. Saing and J. Y. Sinurat, “ANALISIS PROGRAM STUDY TOUR WISATA SEJARAH SEBAGAI SUMBER BELAJAR PADA SISWA DI SMA NEGERI 2 KOTA JAMBI” 2024.
M. R. F. Septiazi and N. Yuliana, “ANALISIS PENGARUH MEDIA SOSIAL TERHADAP GERAKAN BOIKOT PRODUK ISRAEL DI INDONESIA,” Open Access, vol. 2, no. 4, 2023.
A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM,” JDMSI, vol. 2, no. 1, p. 31, Feb. 2021, doi: 10.33365/jdmsi.v2i1.1021.
I. S. Tinendung and I. Zufria, “Pengelompokan Status Stunting Pada Anak Menggunakan Metode K-Means Clustering,” mib, vol. 7, no. 4, p. 2014, Oct. 2023, doi: 10.30865/mib.v7i4.6908.
A. Firdaus and W. I. Firdaus, “Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi : (Sebuah Ulasan),” vol. 13, no. 1, 2021.
M. Furqan, S. Sriani, and S. M. Sari, “Analisis Sentimen Menggunakan K-Nearest Neighbor Terhadap New Normal Masa Covid-19 Di Indonesia,” tc, vol. 21, no. 1, pp. 51–60, Feb. 2022, doi: 10.33633/tc.v21i1.5446.
Sonia Wanda Mafriza and Armansyah, “Klasifikasi karir mahasiswa bidang web developer menggunakan algoritma naïve bayes,” infotech, vol. 4, no. 2, pp. 270–280, Dec. 2023, doi: 10.37373/infotech.v4i2.907.
R. Kurniawan R and I. Zufria, “Penerapan Text Mining Pada Sistem Penyeleksian Judul Skripsi Menggunakan Algoritma Latent Dirichlet Allocation(LDA),” ijcs, vol. 11, no. 3, Dec. 2022, doi: 10.33022/ijcs.v11i3.3120.
Reski Amalia Salam, Tasrif Akib, and Muhammad Dahlan, “Analisis Wacana Kritis terhadap Sarkasme dalam Twitter Sejak Bulan September-November 2023,” Onoma, vol. 10, no. 2, pp. 1855–1863, May 2024, doi: 10.30605/onoma.v10i2.3621.
A. Ma’rif, Buku Ajar Pemrograman Lanjut Bahasa Pemrograman Python. Yogyakarta: Universitas Ahmad Dahlan, 2020.
S. A. Ashari, M. W. A. Saputra, E. Larosa, and B. S. Rijal, “Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore),” jt, vol. 21, no. 2, pp. 168–182, Dec. 2023, doi: 10.37031/jt.v21i2.412.
R. Husna El, R. Wasono, and M. Al Haris, “Analisis Sentimen Pada Twitter Mengenai Netflix Diblokir Telkom Menggunakan Metode Support Vector Machine,” Seminar Nasional Variansi, pp. 214–222, 2020.
D. Hardianti, R. R. Pertama, and A. Abdurahman, “KEPASTIAN HUKUM ATAS HAK CIPTA SEBAGAI OBJEK WAKAF BERDASARKAN HUKUM POSITIF DI INDONESIA,” vol. 5, 2021.
Z. Alhaq, A. Mustopa, S. Mulyatun, and J. Santoso Dwi, “Penerapan Metode Support Vector Machine untuk Analisis Sentimen Pengguna Twitter,” Journal of Information System Management, vol. 3, 2021.
S. Ailiyya, Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Tokopedia Menggunakan Metode Support Vector Machine. Jakarta: UIN Syarif Hidayatullah, 2020.
E. R. Lidinillah, T. Rohana, and A. R. Juwita, “Analisis sentimen twitter terhadap steam menggunakan algoritma logistic regression dan support vector machine,” tekno, vol. 10, no. 2, pp. 154–164, Jul. 2023, doi: 10.37373/tekno.v10i2.440.
M. Ikhsan and R. Kurniawan, “Penerapan Text Mining pada Sistem Rekomendasi Pembimbing Skripsi Mahasiswa Menggunakan Algoritma Naïve Bayes Classifier,” Indonesian Journal of Computer Science, vol. 12, no. 6, 2023.
A. H. Lubis, L. P. A. Lubis, and Sriani, “Sentiment analysis on twitter about the death penalty using the support vector machine method,” TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, vol. 11, no. 2, pp. 312–321, 2024, doi: 10.37373.
R. W. Pratiwi, S. F. H, D. Dairoh, D. I. Af’idah, Q. R. A, and A. G. F, “Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM),” INISTA, vol. 4, no. 1, pp. 40–46, Dec. 2021, doi: 10.20895/inista.v4i1.387.
O. Al Farobey, Implementasi Metode Support Vector Machine Untuk Mengetahui Respon Masyarakat Terhadap Pemberian Vaksin Sinovac. Jakarta: UIN Syarif Hidayatullah, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Sentiment Analysis Study Tour Bus Ban on Twitter Using Support Vector Machine Method
Pages: 842-849
Copyright (c) 2024 Ony Hizri Kaifa Purba, Ilka Zufria
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
